# Evolving Evolutionary Algorithms using Linear Genetic Programming

arXiv: https://arxiv.org/abs/2109.13110 · HTML: https://mlsanity.com/paper/2109.13110/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 44 references that are in the map.
- First posted on arXiv 21 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [Evolving TSP heuristics using Multi Expression Programming](https://mlsanity.com/paper/1509.02459/) (2015)

## Built on by (most cited first, 5 of 5)

- [A Survey of Methods for Automated Algorithm Configuration](https://mlsanity.com/paper/2202.01651/) (2022)
- [Automated Design of Metaheuristic Algorithms: A Survey](https://mlsanity.com/paper/2303.06532/) (2023)
- [AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms with Diverse Structures](https://mlsanity.com/paper/2204.00998/) (2022)
- [New Evolutionary Computation Models and their Applications to Machine Learning](https://mlsanity.com/paper/2110.00468/) (2021)
- [Evolving Evolutionary Algorithms with Patterns](https://mlsanity.com/paper/2110.05951/) (2021)

## Abstract (arXiv)

A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem, and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.